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Sampling Methods: Overview01:06

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Altercasting is a strategic communication technique in which an individual imposes a specific identity or social role onto another person to influence their behavior and shape the interaction. By presuming a role—such as “responsible leader” or “patient person”—altercasting encourages the target to conform to that identity, often aligning their behavior with the expectations associated with the role. The power of this tactic lies in its subtlety; once a role...
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Sampling materials are classified into three main types: solid, liquid, and gas.
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EMM: Plug-and-play memory-bank sampling for contrastive recommendation.

Zhisheng Meng1, Jian Wang2, Lei Li3

  • 1College of Computer Science, University of New South Wales, NSW, 2052, Australia.

Neural Networks : the Official Journal of the International Neural Network Society
|April 10, 2026
PubMed
Summary

Episodic Memory Mining (EMM) enhances contrastive recommendation by strategically sampling negative items. This approach improves representation quality and training stability, especially with sparse user feedback.

Keywords:
Contrastive recommendationHard-negative miningMemory-bank samplingSelf-supervised graph recommendation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Contrastive recommendation models rely on both encoders and sample construction for quality.
  • Challenges include generating informative, fresh, and diverse negative samples and preventing false negatives.

Purpose of the Study:

  • To propose Episodic Memory Mining (EMM) as a principled method for negative sampling in contrastive recommendation.
  • To address the limitations of random sampling and static hard negative mining.

Main Methods:

  • EMM reframes negative sampling as a plug-and-play component, intervening only at the sampling layer.
  • It utilizes an Exponential Moving Average (EMA) memory with periodic rebuilding for fresh negatives.
  • Top-K retrieval from a refreshed pool and a mix of random negatives with rolling refresh stabilize gradients.

Main Results:

  • EMM achieved an average improvement of 15.1% in overall recommendation quality across three benchmarks and five backbones.
  • The method demonstrated comparable training costs and improved hyperparameter robustness.
  • EMM effectively keeps negative samples fresh, informative, and diverse.

Conclusions:

  • Negative sample construction is a critical and generalizable factor for improving contrastive recommendation performance.
  • EMM offers a robust solution to key challenges in negative sampling, enhancing model effectiveness under sparse feedback.